---
title: "Data Viz Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    source_code: embed
    
---

```{r setup, include=FALSE}
library(tidyverse)
library(readxl)
library(ggplot2)
library(plotly)
knitr::opts_chunk$set(message = FALSE)
```

Original Data {vertical_layout=scroll data-icon="fa-archive"}
===================================== 

Row {data-height=10000}
------------------------------------------------------------------------------

### A look at the data

```{r raw data}
EchoC_data <- read_excel("Pfeifer Echo Edit for R.xlsx", sheet = "Echo_for_R")
head(EchoC_data)
summary(EchoC_data)
names(EchoC_data)

```

Column {data-height=10000}
------------------------------------------------------------------------------

### Running some statistics

```{r stats}

stats_1<-lapply(EchoC_data[,4:24], function(x) t.test(x ~ EchoC_data$Genotype, var.equal = TRUE))
stats_1

```


Visualizations {data-orientation=columns data-icon="fa-chart-bar"}
===================================== 
Row {.tabset data-height=400}
------------------------------------------------------------------------------

### Early violin plots 

```{r Early Data}
LV_Sys_Vol_Sept <- ggplot(EchoC_data, aes(x = Genotype, y = LV_Vol_s)) + geom_violin() + theme_bw() + 
  ylab("Left Ventricular Systolic Volume (uL)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant"), label = c("Wild Type", "Mutant")) +
  ggtitle("September 2018 Systolic Volume Variation") + 
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_summary(fun.y = mean, geom="point", shape = 18, size  = 3) + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue")
LV_Sys_Vol_Sept

LV_Diameter_Sept <- ggplot(EchoC_data, aes(x = Genotype, y = Dia_s)) + geom_violin() + theme_bw() + 
  ylab("Left Ventricular Diameter (mm)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant"), label = c("Wild Type", "Mutant")) +
  ggtitle("September 2018 Diameter Variation During Systole") + 
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_summary(fun.y = mean, geom="point", shape = 18, size  = 3) + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue")
LV_Diameter_Sept
```

Row {.tabset data-height=400}
------------------------------------------------------------------------------

### Poster violin plots 

```{r poster graphs}

jn_theme <- function(){
  theme_bw() +
    theme(axis.text = element_text(size = 14, color = "Black"),
          axis.title = element_text(size = 16),
          panel.grid.minor = element_blank(),
          strip.text = element_text(size=14),
          strip.background = element_blank(),
          plot.title = element_text(size = 20, hjust = 0.5),
          panel.grid.major.x = element_blank())
}


LV_Sys_Vol_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = LV_Vol_s)) + 
  geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) + 
  scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
  geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
  ylab("Left Ventricular Systolic Volume (uL)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  stat_summary(fun.y = mean, geom = "point", shape = 5, size  = 3, color = "#9A2617") + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue") +
  jn_theme() +
  theme(axis.title.x = element_blank())
LV_Sys_Vol_15

#Dont need this for poster
LV_Diam_systole_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = Dia_s)) + 
  geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) + 
  scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
  geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
  ylab("Left Ventricular Diameter (mm)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  stat_summary(fun.y = mean, geom = "point", shape = 5, size  = 3, color = "#9A2617") + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue") +
  jn_theme() +
  theme(axis.title.x = element_blank())
LV_Diam_systole_15


LVAW_dia_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = LVAW_d)) + 
  geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) + 
  scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
  geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
  ylab("Left Ventricular Anterior Wall Diameter (mm)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  stat_summary(fun.y = mean, geom = "point", shape = 5, size  = 3, color = "#9A2617") + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue") +
  jn_theme() +
  theme(axis.title.x = element_blank())
LVAW_dia_15

Ejection_fraction_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = EF)) + 
  geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) + 
  scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
  geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
  ylab("Ejection Fraction (%)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  stat_summary(fun.y = mean, geom = "point", shape = 5, size  = 3, color = "#9A2617") + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue") +
  jn_theme() +
  theme(axis.title.x = element_blank())
Ejection_fraction_15


LVOT_Mean_grad_15 <- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = LVOT_mean_grad)) + 
  geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) + 
  scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
  geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
  ylab("Mean Gradient (mmHg)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  stat_summary(fun.y = mean, geom = "point", shape = 5, size  = 3, color = "#9A2617") + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue") +
  jn_theme() +
  theme(axis.title.x = element_blank())
LVOT_Mean_grad_15

Aorta_sys_15<- ggplot(data = EchoC_data[EchoC_data$Group == "September",], aes(x = Genotype, y = Aor_sys)) + 
  geom_violin(scale = "area", adjust = 1, aes(color = Genotype), show.legend = FALSE) + 
  scale_color_manual(values = c("Wild_Type"="Black", "Mutant"="Red")) +
  geom_point(size = 0.5, height = 0, width = 0.05) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
  ylab("Aorta Diameter (mm)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  stat_summary(fun.y = mean, geom = "point", shape = 5, size  = 3, color = "#9A2617") + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue") +
  jn_theme() +
  theme(axis.title.x = element_blank())
Aorta_sys_15
```


Row {.tabset data-height=400}
------------------------------------------------------------------------------

### Tidied paired violin plots 
```{r plots}

jn_theme <- function(){
  theme_bw() +
    theme(axis.text = element_text(size = 14, color = "Black"),
          axis.title = element_text(size = 16),
          panel.grid.minor = element_blank(),
          strip.text = element_text(size=14),
          strip.background = element_blank(),
          plot.title = element_text(size = 20, hjust = 0.5),
          panel.grid.major.x = element_blank())
}


LV_EF <- ggplot(EchoC_data, aes(x = Genotype, y = EF, fill = Group)) + 
  geom_violin(scale = "area", adjust = 1) + 
  scale_fill_manual(values=c("#999999", "#D4AF37", "#56B4E9")) +
  theme_bw() + 
  geom_point(position=position_dodge(width = 0.9), aes(group = Group), size = .5, height = 0, width = 0.05, show.legend = FALSE) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
  ylab("Ejection Fraction %") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  ggtitle("Ejection Fraction Variation") + 
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_summary(fun.y = mean, geom="point", shape = 18, size  = 3, color = "red", position = position_dodge(width = 0.9), show.legend = FALSE) + 
  stat_summary(fun.y=median,  geom = "point", shape = 3, size = 3, color = "blue", position = position_dodge(width = 0.9), show.legend = FALSE) +
  jn_theme()
LV_EF


LVOT_Mean_grad <- ggplot(EchoC_data, aes(x = Genotype, y = LVOT_mean_grad, fill = Group)) + 
  geom_violin(scale = "area", adjust = 1) + 
  scale_fill_manual(values=c("#999999", "#D4AF37", "#56B4E9")) +
  theme_bw() + 
  geom_point(position=position_dodge(width = 0.9), aes(group = Group), size = .5, height = 0, width = 0.05, show.legend = FALSE) + 
  ylab("Left Ventricular Outflow Tract Mean Gradient") + #UNITS???
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  ggtitle("Left Ventricular Outflow Tract Mean Gradient Variation") + 
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_summary(fun.y = mean, geom="point", shape = 18, size  = 3, color = "red", position = position_dodge(width = 0.9), show.legend = FALSE) + 
  stat_summary(fun.y=median,  geom = "point", shape = 3, size = 3, color = "blue", position = position_dodge(width = 0.9), show.legend = FALSE) +
  jn_theme()
LVOT_Mean_grad


Aorta_sys <- ggplot(EchoC_data, aes(x = Genotype, y = Aor_sys, fill = Group)) + 
  geom_violin(scale = "area", adjust = 1) + 
  scale_fill_manual(values=c("#999999", "#D4AF37", "#56B4E9")) +
  theme_bw() + 
  geom_point(position=position_dodge(width = 0.9), aes(group = Group), size = .5, height = 0, width = 0.05, show.legend = FALSE) + 
  ylab("Aorta Diameter (mm)") + #UNITS???
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  ggtitle("Variations in Aortic Diameter in Systole") + 
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_summary(fun.y = mean, geom="point", shape = 18, size  = 3, color = "red", position = position_dodge(width = 0.9), show.legend = FALSE) + 
  stat_summary(fun.y=median,  geom = "point", shape = 3, size = 3, color = "blue", position = position_dodge(width = 0.9), show.legend = FALSE) +
  jn_theme()
Aorta_sys

```





Interactive Plots {data-orientation=columns data-icon="fa-eye"}
===================================== 

Column {data-width=500}
------------------------------------------------------------------------------

### Using plotly

```{r interactive}

jn_theme <- function(){
  theme_bw() +
    theme(axis.text = element_text(size = 14, color = "Black"),
          axis.title = element_text(size = 16),
          panel.grid.minor = element_blank(),
          strip.text = element_text(size=14),
          strip.background = element_blank(),
          plot.title = element_text(size = 20, hjust = 0.5),
          panel.grid.major.x = element_blank())
}


LV_Sys_Vol <- ggplot(EchoC_data, aes(x = Genotype, y = LV_Vol_s, fill = Group, label = ID)) + 
  geom_violin() + 
  xlim(0, 20) +
  scale_fill_manual(values=c("#999999", "#D4AF37", "#56B4E9")) +
  geom_point(position=position_dodge(width = .9), aes(group = Group), size = .5, height = 0, width = 0.05, show.legend = FALSE) + #can use geom_jitter to introduce a small amount of random variation to the location of each point to handle overplott
  ylab("Left Ventricular Systolic Volume (μL)") + 
  scale_x_discrete(limits = c("Wild_Type", "Mutant")) + 
  ggtitle("Systolic Volume Variation") + 
  stat_summary(fun.y = mean, geom = "point", shape = 18, size  = 3, color = "red", position = position_dodge(width = 0.9), show.legend = FALSE) + 
  stat_summary(fun.y = median,  geom = "point", shape = 3, size = 3, color = "blue", position = position_dodge(width = 0.9), show.legend = FALSE) +
  jn_theme()

LV_Sys_Vol




#Messing around:
library(plotly)
ggplotly(LV_Sys_Vol, tooltip = c("ID", "LV_Vol_s"))%>%layout(violinmode = 'group', violingap = 1, violingroupgap = 1)

```


About {data-orientation=rows data-icon="fa-comment-alt"}
===================================== 

Row {data-height=1000}
------------------------------------------------------------------------------

Dashboard made by Jacob Noeker:

* Made for Data Visualization in R
* Thank you to Abhijit Dasgupta